Hydrological Modeling Reproducibility Through Data Management and Adaptors for Model Interoperability

Abstract:

Because of a lack of centralized planning and no widely-adopted standards among hydrological modeling research groups, research communities, and the data management teams meant to support research, there is chaos when it comes to data formats, spatio-temporal resolutions, ontologies, and data availability. All this makes true scientific reproducibility and collaborative integrated modeling impossible without some glue to piece it all together. Our Virtual Watershed Integrated Modeling System provides the tools and modeling framework hydrologists need to accelerate and fortify new scientific investigations by tracking provenance and providing adaptors for integrated, collaborative hydrologic modeling and data management. Under global warming trends where water resources are under increasing stress, reproducible hydrological modeling will be increasingly important to improve transparency and understanding of the scientific facts revealed through modeling.

The Virtual Watershed Data Engine is capable of ingesting a wide variety of heterogeneous model inputs, outputs, model configurations, and metadata. We will demonstrate one example, starting from real-time raw weather station data packaged with station metadata. Our integrated modeling system will then create gridded input data via geostatistical methods along with error and uncertainty estimates. These gridded data are then used as input to hydrological models, all of which are available as web services wherever feasible. Models may be integrated in a data-centric way where the outputs too are tracked and used as inputs to “downstream” models.

This work is part of an ongoing collaborative Tri-state (New Mexico, Nevada, Idaho) NSF EPSCoR Project, WC-WAVE, comprised of researchers from multiple universities in each of the three states. The tools produced and presented here have been developed collaboratively alongside watershed scientists to address specific modeling problems with an eye on the bigger picture of scientific reproducibility and transparency, and data publication and reuse.